Neural Information Processing Systems http://nips.cc/

For all tasks, our method outperforms state-of-the-art methods.

The ability to quickly and accurately identify covariate shift at test time is a critical and often overlooked component of safe machine learning systems deployed in high-risk domains. While methods exist for detecting when predictions should not be made on out-of-distribution test examples, identifying distributional level differences between training and test time can help determine when a model should be removed from the deployment setting and retrained. In this work, we define harmful covariate shift (HCS) as a change in distribution that may weaken the generalization of a predictive model. To detect HCS, we use the discordance between an ensemble of classifiers trained to agree on training data and disagree on test data. We derive a loss function for training this ensemble and show that the disagreement rate and entropy represent powerful discriminative statistics for HCS. Empirically, we demonstrate the ability of our method to detect harmful covariate shift with statistical certainty on a variety of high-dimensional datasets. Across numerous domains and modalities, we show state-of-the-art performance compared to existing methods, particularly when the number of observed test samples is small.

Hateful Meme Challenge proposed by Facebook AI has attracted contestants around the world. The challenge focuses on detecting hateful speech in multimodal memes. Various state-of-the-art deep learning models have been applied to this problem and the performance on challenge's Figure 1: Examples of Hateful Memes [3] leaderboard has also been constantly improved. In this paper, we enhance the hateful detection framework, including utilizing Detectron for feature extraction, exploring different the gap between the best model and human is still large. A setups of VisualBERT and UNITER models with different recent research comparing models of hateful speech detection loss functions, researching the association between the in multimodal memes and human shows an accuracy hateful memes and the sensitive text features, and finally of 64.73% and 84.7%[12], leaving much room for further building ensemble method to boost model performance.

The research team behind Meta AI's Detectron project has recently been awarded the PAMI Mark Everingham Prize for contributions to the computer vision community. We first open-sourced the Detectron codebase five years ago as a collection of state-of-the-art algorithms for tasks such as object detection and segmentation. It has since evolved and advanced in important ways thanks to the contributions of both the open source community and many researchers here at Meta. In 2019, we released a ground-up rewrite of the codebase entirely in PyTorch to make it faster, more modular, more flexible, and easier to use in both research-first and production-oriented projects. Earlier this year, we released Detectron2Go, a state-of-the-art extension for training and deploying efficient object detection models on mobile devices and hardware, as well as significantly improved baselines based on the recently published state-of-the-art results produced by other experts in the field. Several members of the Detectron team sat down to discuss the project's origins, advances, and future.

Deep learning is a very important aspect to learn and understand artificial intelligence. It is a subset of machine learning that processes a large number of datasets, to identify patterns in human behaviour. Deep learning algorithms are trained in a manner that it accumulates, analyses and processes exponential datasets, without any human intervention. Owing to its robust mechanism, it is getting promptly adopted across the industry. Understanding AI has become one of the most demanded skills across the industry. To fully comprehend the peculiarities of AI, deep learning models come in handy.

Understanding the AI's peculiarities, beginners must understand the examples of deep learning models used across the industry. Deep learning is a very important aspect to learn and understand artificial intelligence. It is a subset of machine learning that processes a large number of datasets, to identify patterns in human behaviour. Deep learning algorithms are trained in a manner that it accumulates, analyses and processes exponential datasets, without any human intervention. Owing to its robust mechanism, it is getting promptly adopted across the industry. Understanding AI has become one of the most demanded skills across the industry.

We propose learning graph representations from 2D feature maps for visual recognition. Our method draws inspiration from region based recognition, and learns to transform a 2D image into a graph structure. The vertices of the graph define clusters of pixels ("regions"), and the edges measure the similarity between these clusters in a feature space. Our method further learns to propagate information across all vertices on the graph, and is able to project the learned graph representation back into 2D grids. Our graph representation facilitates reasoning beyond regular grids and can capture long range dependencies among regions. We demonstrate that our model can be trained from end-to-end, and is easily integrated into existing networks. Finally, we evaluate our method on three challenging recognition tasks: semantic segmentation, object detection and object instance segmentation. For all tasks, our method outperforms state-of-the-art methods.

This project aims at providing the necessary building blocks for easily creating detection and segmentation models using PyTorch 1.0. A notebook with the demo can be found in demo/Mask_R-CNN_demo.ipynb. Check INSTALL.md for installation instructions. Pre-trained models, baselines and comparison with Detectron and mmdetection can be found in MODEL_ZOO.md We provide a helper class to simplify writing inference pipelines using pre-trained models.

A few weeks ago, Facebook open-sourced its platform for object detection research, which they are calling Detectron. Object detection, wherein a machine learning algorithm detects the coordinates of objects in images, remains an ongoing challenge. To find algorithms that provide both sufficient speed and high accuracy is far from a solved problem. Detectron's ostensible purpose is to move a bit closer to that goal using community-led contributions. Google did something similar last year when they released the Tensorflow Object Detection API, which Filestack utilizes for its object detection models, so we thought we'd take a look at Detectron and see how it compares.